Google Exam Professional Data Engineer Topic 3 Question 71 Discussion

Actual exam question for Google's Professional Data Engineer exam

Question #: 71
Topic #: 3

[All Professional Data Engineer Questions]

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

a. What should you do?

ABuild and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.

BBuild and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the models
using Cloud Dataproc. Call the models from your application.

CBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.

DBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Show Suggested Answer

Suggested Answer: C

by Lucy at Oct 09, 2023, 05:21 AM

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Audra

16 days ago

As a video enthusiast, I'd love to see a recommendation engine that can keep up with my binge-watching habits! Option C sounds like it would be a 'reel' winner.

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1 months ago

Option D seems like a good choice if you're already familiar with Cloud SQL. The simplicity of storing data in a relational database and joining the labels with user history could be a straightforward approach.

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Rashida

3 days ago

But wouldn't using the Cloud Video Intelligence API to generate labels be more accurate?

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Nina

4 days ago

I agree, using Cloud SQL for storing data and joining labels with user history sounds efficient.

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Kenia

27 days ago

Option D seems like a good choice if you're already familiar with Cloud SQL.

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2 months ago

Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.

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Mary

12 days ago

Yes, using Cloud Bigtable for storing data and filtering predicted labels based on user viewing history is a smart choice for this recommendation engine application.

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Arthur

16 days ago

It's important to have fast filtering suggestions based on customer preferences, and Cloud Bigtable can definitely help with that.

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Jeffrey

22 days ago

I agree, Cloud Video Intelligence API can help generate accurate labels for the videos, and Cloud Bigtable can handle the massive amount of data efficiently.

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Colene

23 days ago

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Patrick

2 months ago

I think option A is the best choice. Using Spark MLlib for classification and Cloud Dataproc for deployment seems efficient.

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